In order to accurately produce textual outputs from speech inputs, speech recognition applications rely on quality audio inputs. Relatively slight imperfections in an audio input can result in significant inaccuracies in generated text. To improve the quality of audio speech inputs, voice recognition applications can perform pre-filtering operations that filter raw audio to minimize background or ambient noise while maximizing the speech component of an audio input. By performing speech-to-text conversions on filtered audio input instead of raw unfiltered input, substantially improved textual outputs can result.
Properly optimizing audio input for speech recognition tasks can be challenging, primarily due to the need to match optimization settings with the acoustic characteristics of an operational environment. Problematically, a wide variety of environments exist over which optimization routines must operate. Notably, environmental considerations can be based on audio hardware as well as acoustic characteristics of the environment in which a speech recognition application must operate. For example, the sensitivity and clarity of a microphone used to gather audio input can substantially affect resulting audio signals. Additionally, the background noise of an environment, which can range from a relative quiet room, to a noisy office, to loud traffic conditions, such as those found in airports, can dramatically affect audio inputs.
To account for vastly different environmental characteristics, audio pre-filtering applications can utilize a variety of optimization algorithms. Behavior of these optimization algorithms can generally be adjusted for specific environmental conditions through the use of configurable optimization parameters. Precisely tuning optimization parameters can be facilitated through optimization tools. Conventional optimization tools, however, suffer from numerous shortcomings.
For example, many of the most precise optimization tools and techniques can require expensive, resource intensive hardware that may be available within a laboratory setting but are not generally available in the field. Since evaluating the effectiveness of optimization parameters settings can require inputs only obtainable at a field location, such lab intensive tools can be ineffective as well as costly. Unfortunately, the optimization tools available at field locations generally do not allow technicians to synchronously compare an input signal, a resulting output signal, and adjustment details. Consequently, technicians often improperly adjust optimization parameters causing ambient noise components to be amplified or speech components to be removed from the audio input.